Spatial Hierarchical Modeling of Precipitation Extremes From a Regional Climate Model

The goal of this work is to characterize the extreme precipitation simulated by a regional climate model (RCM) over its spatial domain. For this purpose, we develop a Bayesian hierarchical model. Since extreme value analyses typically only use data considered to be extreme, the hierarchical approach is particularly useful as it sensibly pools the limited data from neighboring locations. We simultaneously model the data from both a control and future run of the RCM which allows for easy inference about projected change. Additionally, this hierarchical model is the first to spatially model the shape parameter which characterizes the nature of the distribution’s tail. Our hierarchical model shows that for the winter season, the RCM indicates a general increase in 100-year precipitation return levels for most of the study region. For the summer season, the RCM surprisingly indicates a significant decrease in the 100-year precipitation return level.     

A Spatio-Temporal Downscaler for Output From Numerical Models

Often, in environmental data collection, data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data but consequential calibration concerns. The second tends to be sparsely collected in space with coarser temporal resolution, often with missing data but, where recorded, provides, essentially, the true value. Accommodating the spatial misalignment between the two types of data is of fundamental importance for both improved predictions of exposure as well as for evaluation and calibration of the numerical model. In this article we propose a simple, fully model-based strategy to downscale the output from numerical models to point level. The static spatial model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially-varying coefficients which are specified through a correlated spatial Gaussian process.     

Continuous Spatial Process Models for Spatial Extreme Values

We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme value (GEV) distributions, with parameters μ, σ, and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independence assumption in the first stage of the hierarchial model, an assumption which has been adopted in previous work. This assumption implies that realizations of the the surface of spatial maxima will be everywhere discontinuous. For many phenomena including, e.g., temperature and precipitation, this behavior is inappropriate. Instead, we offer a spatial process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters. In this sense, the first stage smoothing is viewed as fine scale or short range smoothing while the larger scale smoothing will be captured in the second stage of the modeling. In addition, as would be desired, we are able to implement spatial interpolation for extreme values based on this model. A simulation study and a study on actual annual maximum rainfall for a region in South Africa are used to illustrate the performance of the model.     

Semiparametric geographically weighted response curves with application to site-specific agriculture

Lack of basic knowledge about spatial and treatment varying crop response to irrigation hinders irrigation management and economic analysis for site-specific agriculture. One model that has been postulated for relating crop-specific economic quantities to irrigation is a quadratic response curve of yield as a function of irrigation. Although this model has far reaching economic interpretations it does not account for spatial variation or possible nitrogen-irrigation interactions. To this end we propose a spatially treatment varying coefficient model that alleviates these limitations while providing measures of uncertainty for the estimated coefficient surfaces as well as other derived quantities of interest. The modeling framework we propose is of independent interest and can be used in many different applications. Finally, an example involving site-specific agricultural data from the U.S. Department of Agriculture-Agricultural Research Service demonstrates the applicability of this methodology.     

Modelling local-scale determinants and the probability of microarthropod species occurrence in Antarctic soils

Soil fauna in the extreme conditions of Antarctica consists of a few microinvertebrate species patchily distributed at different spatial scales. Populations of the prostigmatic mite Stereotydeus belli and the collembolan Gressittacantha terranova from northern Victoria Land (Antarctica) were used as models to study the effect of soil properties on microarthropod distributions. In agreement with the general assumption that the development and distribution of life in these ecosystems is mainly controlled by abiotic factors, we found that the probability of occurrence of S. belli depends on soil moisture and texture and on the sampling period (which affects the general availability of water); surprisingly, none of the analysed variables were significantly related to the G. terranova distribution. Based on our results and literature data, we propose a theoretical model that introduces biotic interactions among the major factors driving the local distribution of collembolans in Antarctic terrestrial ecosystems.     

近20年黄河流域植被净初级生产力时空分布及其对极端天气变化的时空响应

为探究黄河流域植被净初级生产力(Net Primary Productivity, NPP)变化及对极端天气指数的响应情况,利用MODIS NPP数据和极端气候数据,辅以斜率法和偏相关分析法分析了2000—2019年黄河流域植被NPP时空动态及其对极端降水指数和极端温度指数的响应情况。结果表明：(1)近20 a黄河流域植被NPP呈从北向南增加的趋势,大面积上表现为增加趋势。(2) NPP与极端降水指标以显著和极显著正相关为主。其中,除最长连续湿润天数呈显著正相关和极显著正相关的像元占比为16.5%,其他几种指标均在25%以上。(3)极端降水事件的水量和强度呈显著增加趋势,极端温度事件中与偏冷相关指标总体呈下降趋势,与变暖有关的事件呈明显上升趋势,其变化存在显著空间异质性,年际差异大。(4)日最高气温的最大值、日最低气温的最大值、冷夜日数、冷昼日数、冰冻天数和霜冻天与植被NPP以负相关为主,日最高气温的最小值、日最低气温的最小值、暖夜日数、暖昼日数、气温日较差和暖期与植被NPP以正相关为主。近20 a黄河流域植被NPP时空变化存在显著地域性差异,其中在干旱和半干旱地区极端降水增多在一定程...     

Analysis of Genebank Evaluation Data by using Geostatistical Methods

Genebanks often characterize accessions based on evaluation trials. This paper evaluates geostatistical methods as a tool to increase the utility of evaluation data. These methods were selected to overcome limitations resulting from a relative lack of replication and the scarcity of standards or check varieties. The data employed in the present study comprise nine characteristics of spring and winter barley, evaluated mostly as ratings. Ratings with quasi-metric scales were transformed by using the folded exponential transformation. To estimate the genetic component of the total effect, we compared two methods: Method 1 whereby a variogram is fitted by non-linear regression, and subsequently the implied spatial correlation is embedded into a mixed model analysis, which estimates the genetic effect by Best Linear Unbiased Prediction (BLUP); and Method 2 where each data value is re-estimated by kriging to correct for spatial effects and then the corrected data are submitted to a mixed model analysis. For practical application we propose Method 1 (though occasionally we met convergence problems): Fit the short range of the empirical variogram, visually choose the suitable covariance model. Use this and the initial values from non-linear regression fit with the mixed model, fixing the spatial parts at their starting values from non-linear regression, and estimate genetic effects by BLUP by using the fitted mixed model. To improve performance, we recommend that more standard or check varieties be used and, wherever possible, replace rating scales with metric scales or free-percentage scales (without categories).     

Examining Extreme Weather Effects on Birth Weight From the Individual Effect to Spatiotemporal Aggregation Effects

Extreme weather events are related to low birth weight. Monitoring this relationship in the context of climate change has a wide range of public health implications, as birth weight is a key indicator of many life course health outcomes, and climate change increases both frequency and intensity of extreme weather events. However, most birth weight data are not available with sufficient spatial and temporal resolution. The current study examined the relationship between birth weight and weather variables in a series of aggregations, from individual birth outcomes to month-county, season-county, and county-only mean birth weights. Data were based on a 20?% sample of White mothers aged 19 to 38 from the United States Natality Data Files, and the baseline model was for the 1974?C1978 and 1984?C1988 periods with 2,269,009 and 2,652,552 individual birth records, respectively. The evaluation was based on multiple regression for aggregation effects, and conditional autoregressive and spatial association models for spatial clustering effects. The results show that the number of extreme cold and hot days during the birth month is inversely associated with birth weight, and that temporal aggregation by month-county or season-county was likely to preserve the relationship between birth weight and extreme weather from the individual model. While both conditional autoregressive and spatial association models can remove some spatial autocorrelation, the spatial association approach may not work effectively without further modifying the existing method.     

基于稀疏编码金字塔模型的农田害虫图像识别

相较于一般物体的图像,农作物害虫图像因具有复杂的农田环境背景,分类与识别更加困难。为提高害虫图像识别的准确率,该文提出一种基于图像稀疏编码与空间金字塔模型相结合的害虫图像表示与识别方法。该方法利用大量非标注的自然图像块构造过完备学习字典,并运用该学习字典实现对害虫图像的多空间稀疏表示。与此同时,结合多核学习,该文设计了一种害虫图像识别算法。通过对35种害虫的识别,试验结果表明：在相同方法下,该文所提特征提取方法可使平均识别精度提高9.5百分点;此外,进一步通过对221种昆虫及20种蝴蝶的识别,试验结果表明：与传统方法相比较,该文所提方法使得平均识别精度提高14.1百分点。     

Spatial Regression Modeling for Compositional Data With Many Zeros

Compositional data analysis considers vectors of nonnegative-valued variables subject to a unit-sum constraint. Our interest lies in spatial compositional data, in particular, land use/land cover (LULC) data in the northeastern United States. Here, the observations are vectors providing the proportions of LULC types observed in each 3 km×3 km grid cell, yielding order 10⁴ cells. On the same grid cells, we have an additional compositional dataset supplying forest fragmentation proportions. Potentially useful and available covariates include elevation range, road length, population, median household income, and housing levels. We propose a spatial regression model that is also able to capture flexible dependence among the components of the observation vectors at each location as well as spatial dependence across the locations of the simplex-restricted measurements. A key issue is the high incidence of observed zero proportions for the LULC dataset, requiring incorporation of local point masses at 0. We build a hierarchical model prescribing a power scaling first stage and using latent variables at the second stage with spatial structure for these variables supplied through a multivariate CAR specification. Analyses for the LULC and forest fragmentation data illustrate the interpretation of the regression coefficients and the benefit of incorporating spatial smoothing.     

Large wind speeds: Modeling and outlier detection

This article addresses the problem of modeling extreme wind speeds with the aim of developing procedures that can be used to reliably identify outliers. There are several approaches to fitting extremes, including using maxima over a fixed time period or taking all observations over a threshold. Using two sets of oceanic wind data from buoys, we use robust estimation methods to estimate the parameters of the asymptotic distribution for extremes over fixed time periods and peaks over threshold. For both cases we also use a gh distribution which focuses on modeling the quantiles and propose a robust method for fitting the data to the gh distribution. Weights from the robust fits are used to identify outliers with P values being computed by resampling. We also evaluate the fits of the data to the model distributions according to several criteria concluding that the gh distribution is at least as effective in fitting the tail behavior as the more classical generalized extreme value distribution and the generalized Pareto distribution.     

An Efficient Approach to Spatiotemporal Analysis and Modeling of Air Pollution Data

A statistically efficient approach is adopted for modeling spatial time-series of large data sets. The estimation of the main diagnostic tool such as the likelihood function in Gaussian spatiotemporal models is a cumbersome task when using extended spatial time-series such as air pollution. Here, using the Innovation Algorithm, we manage to compute it for many spatiotemporal specifications. These specifications refer to the spatial periodic-trend, the spatial autoregressive moving average, the spatial autoregressive integrated and fractionally integrated moving average Gaussian models. Our method is applied to daily pollutants over a large metropolitan area like Athens. In the applied part of our paper, we first diagnose temporal and spatial structures of data using non-likelihood based criteria, such as the empirical autocorrelation and covariance functions. Second, we use likelihood and non-likelihood based criteria to select a spatiotemporal model among various specifications. Finally, using kriging we regionalize the resulting parameter estimates of the best-fitted model in space at any unmonitored location in the Athens region. The results show that a specific autoregressive integrated moving average spatiotemporal model can optimally perform in within and out of spatial sample estimation. Supplemental materials for this article are available from the journal website.     

Hierarchical Bayesian Spatio-Temporal Conway–Maxwell Poisson Models with Dynamic Dispersion

Modeling spatio-temporal count processes is often a challenging endeavor. That is, in many real-world applications the complexity and high-dimensionality of the data and/or process do not allow for routine model specification. For example, spatio-temporal count data often exhibit temporally varying over/underdispersion within the spatial domain. In order to accommodate such structure, while quantifying different sources of uncertainty, we propose a Bayesian spatio-temporal Conway–Maxwell Poisson (CMP) model with dynamic dispersion. Motivated by the problem of predicting migratory bird settling patterns, we propose a threshold vector-autoregressive model for the CMP intensity parameter that allows for regime switching based on climate conditions. Additionally, to reduce the inherent high-dimensionality of the underlying process, we consider nonlinear dimension reduction through kernel principal component analysis. Finally, we demonstrate the effectiveness of our approach through out-of-sample one-year-ahead prediction of waterfowl migratory patterns across the United States and Canada. The proposed approach is of independent interest and illustrates the potential benefits of dynamic dispersion in terms of superior forecasting. This article has supplementary material online.     

2011-2050年贵州省极端气候指数时空变化特征

为探究未来贵州省极端气候变化发生趋势与空间格局,在利用中国天气发生器NCC/GU-WG Version 2.0模型对2011-2050年逐日降水量、日最高温和日最低温进行预测的基础上,运用ArcGIS软件和变化趋势分析法,分析了贵州省2011-2050年极端气候指数时空变化特征。结果表明:40年间,日最高温气温（TXx）、日最低气温（TNn）、暖日指数（TX90p）和持续暖期（WSDI）每10 a分别增加0.1℃,0.03℃,0.23 d和0.4 d;冷日指数（TX10p）和持续冷期（CSDI）每10 a分别下降0.1 d和0.26 d。最大日降水量（RX1day）、5日最大降水量（R5D）、强降水量（R95T）、日降水量强度（SDⅡ）和连续湿日（CDD）每10 a分别增加1.02 mm,1.31 mm,5.63 mm,0.01 mm/d和0.05 d,连续干日（CWD）每10 a下降0.11 d。极端气候指数及其变化趋势的空间格局异质性突出。气候变暖和地形是影响贵州省极端气候指数变化的主要因素。     

基于CMIP6气候模式的贵州省极端降水情景预估

为探究气候变化下贵州省极端降水未来变化特征,基于台站观测和5个CMIP6模式的逐日降水资料,利用Delta降尺度、趋势分析等方法,分析了4种极端降水指数的历史与未来变化时空特征。结果表明：(1)利用Delta修正过后的CMIP6模式数据取得了良好的效果,适用于贵州省极端降水的预估。(2)在1961—2019年的历史时期,贵州省的R95P,R25mm和CWD均呈南高北低的空间分布,而R95C呈明显的东中西差异;除CWD外,其它3个极端降水指数在1961—2019年均呈不明显的增加趋势。(3)未来3种SSPs情景下,无论是在时间还是空间上,4个极端降水指数均呈上升的趋势。(4)相较于历史时期,4个极端指数除R95C外均有增有减。R95P与R25mm在空间变化上相似,都表现为西北部较历史时期有减少,其余地区则表现为增多,且随SSPs升高而增大;CWD在中南部地区表现为减少,其余地区为增加,以北部较为明显,且在SSP126情景下最为显著;R95C则在整个地区都较历史时期增多,在中西部变化最明显,且在SSP245情景下最显著。总体而言,在气候变化背景下,贵州省的极端降水随SSPs的不同而变化,但...     

甘肃省极端降水指数时空变化特征

为了明确甘肃省极端降水事件的时空发生规律和变化特征,根据气候变化监测与指数专家组(ETCCDI)推荐的11个极端降水指数,分析了极端降水指数的时间变化特征,基于GIS技术采用空间地统计方法,研究了极端降水指数空间分布特征。结果表明：在时间分布上,持续干期呈显著减弱,变化速率为-2.129(p<0.05),持续湿期微弱下降,变化速率为-0.005(p<0.05),其余10个极端降水指数呈增强趋势(p<0.05),尤其年降水总量增加显著,变化速率为2.56(p<0.05)。总体来看,极端指数变化存在明显周期性,大致可分为2～3个周期;突变多发生在20世纪60年代中后期。在空间分布上,全省极端降水指数的空间变化特征较为明显,包括年降水量在内的10个极端指数由河西走廊向东递增,持续干期与之相反。综上,甘肃省极端降水指数变化具有明显的时间和空间差异。     

基于空间插值逐日降水格点数据的福建省降水时空变化分析

构建可靠的高时空分辨率降水数据集,揭示全球变暖背景下的降水时空变化特征,对于水资源管理与水土流失预防与治理至关重要。利用组合空间插值方法,以福建省1979—2018年400余个站点观测逐日降水数据为原始数据源,得到研究区0.05°×0.05°高空间分辨率逐日降水格点数据集。以此数据集为基础,计算8个极端降水指数和3个降水集中程度指标,分析福建省降水时空变化特征。结果表明：提出的组合空间插值方法可以有效提高逐日降水插值精度,并且数据精度高于目前常用的再分析与卫星遥感降水数据产品;福建沿海地区、闽江下游1日最大降水量、5日最大降水量、强降水量、降水总量、降水强度5个极端降水指标有大面积显著上升趋势;全区域降水集中期以鹫峰山脉-闽江下游-戴云山脉一线为界,西北地区早于6月11日,东南地区则晚于6月11日,与福建省前后汛期时段基本相符;西北地区前汛期雨季有后推趋势,东南地区后汛期雨量有增多趋势。     

Change-Point Estimation in the Multivariate Model Taking into Account the Dependence: Application to the Vegetative Development of Oilseed Rape

In this paper, we address the change-point estimation issue in multivariate observations which consist in functions having piecewise constant first derivatives corrupted by some additional noise. We propose to solve this problem by rewriting it as a variable selection issue in a sparse multivariate linear model. Moreover, the methodology that we propose takes into account the dependence that may exist within the multivariate observations. Then, the performance of our approach is assessed through some numerical experiments and compared to other alternative and classical methods. Finally, we apply our methodology to experimental data in order to study the vegetative development of oilseed rape. The evolution of the number of leaves of oilseed rape can be modeled as a function having piecewise constant first derivatives corrupted by some additional noise where the change-points correspond to key times in the plant phenology. Our novel estimation method increases the accuracy of the change-point estimation in comparison with classical approaches. Moreover, we show that the parameters of the covariance matrix depend on the level of competition between plants. Supplementary materials accompanying this paper appear online.